NIDA National Early Warning System Network http://wiki.knoesis.org/index.php/NIDA_National_Early_Warning_System_Network_(iN3)

1. Exploring Synthetic Cannabinoid Effects
Using Web Forum Data
(NIDA National Early Warning System Network)
Robert G. Carlson1,2, Francois Lamy1,2, Amit Sheth2, Raminta
Daniulaityte1,2
1Center for Interventions, Treatment & Addictions Research
Wright State University Boonshoft School of Medicine, Dayton, OH
2Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Department of
Computer Science and Engineering, Wright State University, Dayton, OH, United States
ITAR
CTreatment & Addictions Research
Center for Interventions,

2. Acknowledgments
• R56 DA038366 “NIDA National Early Warning
System Network (iN3): An Innovative Approach”
• Robert G. Carlson1,2, PI; Amit Sheth2, PI; Edward Boyer3, PI;
Raminta Daniulaityte1,2, Co-I; Jeffrey Brent, 4 Co-I; Paul Wax,
Co-I4
• 1Center for Interventions, Treatment & Addictions Research
• Wright State University Boonshoft School of Medicine, Dayton, OH
• 2Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis),
Department of Computer Science and Engineering, Wright State University,
Dayton, OH, United States
• 3Department of Emergency Medicine, University of Massachusetts Medical
School, Worcester, MA, United States
• 4American College of Medical Toxicology, Phoenix, AZ, United States

3. Acknowledgments
• R01 DA039454 “Trending: Social Media Analysis to Monitor
Cannabis and Synthetic Cannabinoid Use”
• Raminta Daniulaityte1,2, PI; Amit Sheth2; PI; Edward Boyer, 3 Co-I;
Robert Carlson,1,2 Co-I; Ramzi Nahhas,4 Co-I; Silvia S. Martins, Co-I5
• 1Center for Interventions, Treatment & Addictions Research, Wright State University
Boonshoft School of Medicine, Dayton, OH
• 2Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis),
Department of Computer Science and Engineering, Wright State University, Dayton,
OH, United States
• 3Department of Emergency Medicine, University of Massachusetts Medical School,
Worcester, MA, United States
• 4Center for Global Health, Department of Community Health, Wright State University
Boonshoft School of Medicine, Dayton, OH
• 5Department of Epidemiology, Columbia University Mailman School of Public Health,
New York, NY

4. Acknowledgments
• The content is solely the responsibility of the authors and
does not necessarily represent the official views of the
National Institute on Drug Abuse or the National
Institutes of Health.
• The authors have no Conflict of Interest to declare.

5. R56 Specific Aims
• 1) Identify new episodes of emerging drug use in
multiple community-level indicators;
• 2) Disseminate information about occurrence,
identity, clinical, and adverse effects of emerging
drug use;
• TWO DATA STREAMS:
–1) Data on Synthetic Cannabinoids and other Novel
Synthetics from 41 Emergency Department sites across
the US (American College of Medical Toxicology);
–2) Data from Twitter and drug Web forums.

6. Rationale to use Web-forum posts
• SCRA users are difficult to reach;
• Most knowledge comes from Poison Control Centers
or Emergency Rooms;
• Mining drug-focused web-forums offers the possibility
to collect data from users freely expressing their
opinions and experiences;
• PRESENTATION AIM: We present eDrugTrends data
on Web forum posts related to Synthetic
Cannabinoids and their effects.

7. Data Sources
• We deployed the eDrugTrends Web forum data
collection and processing features;
• Data were collected from Bluelight, Drugs-forum, and
Reddit;
• Bluelight website is a partner in the R01 study.

8. Method
Entity Matching (NLP Python)
Data Access Tool (Kibana) Drug-oriented Ontology (DUAO)Data Collection
Frequency Distribution
Positive
effects Searched Terms Percentages
High high, stoned 44.7%
Trip
trip, tripping, hallucinating, visual
distortions, hearing things, psychedelic 12.8%
Euphoric
euphoric, euphoria, rush, buzz, pleasure,
bliss 7.7%
Sleep
sleep, asleep, sleepy, sedative, sedation,
sedate 6.3%
Calm calm, calming, relaxant 1.8%
Energy energy, energetic, stimulated, alertness 1.1%

9. Drug Abuse Ontology (DAO)
• Ontology is a conceptualization of all the elements that
belong to a specific domain;
• DAO currently encompasses 944 drug-related terms
(184 SCRA terms) as well as 419 positive and
negative drug-related effect terms;
• DAO Enables entity recognition within posts and co-
occurrences of several concepts.

10. Type of
keywords
Individual Entity Examples
Chemical
name
JWH-018; JWH-073; CP-47,497; HU-210; WIN-55; JWH-200; CCH;
JWH-250; AM-2201; JWH-210; JWH-122; JWH-203; AM-2233;
JWH-019; UR-144; APICA; FUBINACA; CHMINACA; PB-22;
PINACA; AKB-48; THJ-018; STS-135; BB-22; BB-25; 5F-MN; 5F-
AKB; 5F-AMB; 5F-ADB; PB-22; NM-2201; SDB-006; 5F-SDB…
Commercial
names
“Black Mamba”; “Tribal Warrior”; “Mr. Kush”; “Mad Hatter”; “Afghan
Black”; “Atomic Bomb”*; Clockwork Orange”*; “Bamboo”*; “Voodoo”*;
“Ultimate Warrior”; “EX-SES”; “Blue Cheese”*; “Bizzaro”…
General
names
“Spice”; “K2”; “noid”; “SCRA”; “synthetic cannabis”; “pot-pourri”;
“herbal incense”; “fake weed”…
*Because keywords could be used in general discussion (e.g., “Clockwork
Orange” as a reference to Stanley Kubrick movie), we ensured that this
mention was related to SCRA by combining these searched terms with the
term “cannabinoid”.
Ontology-based SCRA Search Terms

11. Ontology-based Side-effect Search Terms
Categories Individual Entities Examples
Acute
Respiratory
Acute Exacerbation, Apnea, Bronchitis, Bronchospasm, Dyspnea,
Pneumonitis, Respiratory Depression, Slow Breathing, Shallow
Breathing, Suffocation,….
Chronic
Respiratory
Asthma, Chronic Cough, Cough all the time, Lung Cancer
Acute
Cognitive
Alertness, Anterograde Amnesia, Auditory Distortions, Closed-Eye
Visualizations, Confusion, Diplopia, Hallucination, Impaired
Reflexes….
Chronic
Cognitive
Memory Dysfunction, Amnesia.
Acute
Nervous
System
Anxiety, Ataxia, Clonus, Dizziness, Drowsiness, Euphoria,
Excitoxicity, Exhilaration, Headache, Pain, Panic Attack, Sedation,
Seizure,….
Social
Related
Aggressiveness, Crime, Decreased Work Performance, Empathy,
Felony, Unsafe sex…
*Misspellings are frequent in web-forum posts. Hence, some side effect keywords were collected using
“fuzzy query” based on Levenshtein edit distance. This type of query guaranties that misspelled words
(such as “siezure” instead of “seizure”) would be captured by our data query.

12. Data collection
• The eDrugTrends platform extracted 19,700,000+
drug-related posts from our data sources from
01/01/2008 until 09/30/2015;
• 43,506 posts containing SCRA Search Terms
found;
–Reddit: 25,981; Drugs-forum: 9,271; Bluelight:
8,254
• 19,728 users shared their thoughts and
experiences on SCRA online.

13. Distribution of SCRA posts over time
0
500
1000
1500
2000
2500
Jan-08 May-09 Sep-10 Feb-12 Jun-13 Nov-14
Posts Users

14. Frequency of SCRA-related Effects
• To ensure recognized effects are related to SCRAs,
we isolated 18,617 posts (n=42.8%) containing no
references to other drugs;
• Why? Because if not cannot presently determine if
effects are related to SCRAs or another drug (s).
• Among these, 4,638 posts (n=24.9%) contained one
or several effects.
–42.4% only negative effect(s), 38.2% only positive
effect(s) and 19.4% both effects

15. Frequency of SCRA-related Positive Effects
Positive
effects Searched Terms Percentages
High high, stoned 44.7%
Trip
trip, tripping, hallucinating, visual
distortions, hearing things, psychedelic 12.8%
Euphoric
euphoric, euphoria, rush, buzz, pleasure,
bliss 7.7%
Sleep
sleep, asleep, sleepy, sedative, sedation,
sedate 6.3%
Calm calm, calming, relaxant 1.8%
Energy energy, energetic, stimulated, alertness 1.1%

16. Negative
Effects Searched Terms Percentages
Death die, dead, death, dying 12.8%
Anxiety anxiety, panic attack, anxious, anxiety attack 10.6%
Nausea
nausea, vomiting, threw up, puking, vomit, throwing up,
puke, throw up, vomited 6.7%
Overdose overdose, od, overdosed, oded, od's 6.6%
Seizure seizure, seizures 5.6%
Addiction addicted, craving, addictive 4.9%
Psychosis paranoia, psychosis, schizophrenia 4.6%
Pain pain, in pain 3.9%
Headache headaches, headache, migraine 3.3%
Confusion
confusion, dizzy, dizziness, drowsiness, light headed,
nodding, drowsy, lightheaded, brain fog 2.6%
Frequency of SCRA-related Negative Effects

17. An example of Entity Recognition
• Personally I found AM-2201 extremely trippy in
headspace and visual components at high doses.
[…] That was the scariest moment I've ever had on
a noid EVER out of my 6 years of smoking noids.
I sweated profusely from head to toe, heart raced
at about 150-155 BPM, and I was having a major
anxiety attack (and I don't have anxiety
problems). […] Because of the extremely addictive
properties of AM-2201 I went through 300mg in 5
days. […] I found the effects of AM-2201 not only
psychedelic but very dissociating mentally from
reality.

18. SCRA Positive and Negative Effects Posts:
changes over time
-20
0
20
40
60
80
100
120
Dec-07 Apr-09 Sep-10 Jan-12 Jun-13 Oct-14
Chart Title
Negative Positive Positive+Negative

19. Discussion
• Decrease in the number of posts starting end of 2012
corresponds with the overall trend of SCRA use as
identified in other sources (NIH, 2015; UNODC,
2016);
• Cannot conclude that SCRA use is decreasing from
an Epi standpoint. Only that people who write to drug
web forums are doing so less.
• Effects extracted from web data are similar to clinical
data (Castellanos & Gralnik, 2016);
• Ability to obtain information about patterns of SCRA
use among a hidden population.
• Potential for much deeper analyses of texts.

20. Limitations
• Entity Recognition based on ”bag of words”
rather than semantic relation;
• Demographic and Geographic information
rarely displayed by web-forum users;
• Polydrug use represents major challenges for
Entity Relation Extraction.

21. Future Steps
• Entity relation extraction to ensure the semantic
relation between ontological concepts;
• E.G., Entity recognition of effects based on body
parts/organs to capture specificity of negative effects;
• Implement Machine Learning trained by domain
experts to increase information extraction accuracy.
(Disambiguation issues)
• Advancements in technical dimensions will enable
addressing a wide array of drug abuse research
questions, such as dose and route of administration,
addiction/withdrawal, and perceived need for
treatment.
• Web-based surveys will enable key insight into
demographics and geolocation.

22. THANK YOU
• Contact Details:
– robert.carlson@wright.edu
– francois.lamy@wright.edu
– Amit.sheth@wright.edu
– Raminta.daniulaityte@wright.edu
• References:
– Castellanos, Daniel et Gralnik, Leonard M. Synthetic cannabinoids 2015:
An update for pediatricians in clinical practice. World journal of clinical
pediatrics, 2016, vol. 5, no 1, p. 16.
– Johnston, Lloyd D., O'Malley Patrick M., Bachman, Jerald G., et al.
Monitoring the Future: National Survey Results on Drug Use, 1975-2005.
Volume 1: Secondary School Students, 2005. NIH Publication No. 06-
5883. National Institute on Drug Abuse (NIDA), 2006.
– United Nations Office on Drugs and Crime, World Drug Report 2015
(United Nations publication, Sales No. E.15.XI.6).